PageRank: A modified random surfer model

Author

Keong, Boo Vooi ; Anthony, Patricia

Author_Institution

UMS-MIMOS Center of Excellence in Semantic Agents, UniversitiMalaysia Sabah, Kota Kinabalu, Malaysia

fYear

2011

fDate

12-13 July 2011

Firstpage

1

Lastpage

6

Abstract

PageRank is an approach to evaluate the importance of a web page implemented by Google. It is one of the important system features that Google used in order to improve the quality of search result in the original version of Google apart from utilizing link (anchor) in web pages. The success of Google has led to various researches on the theory behind Google search. PageRank is one of the theories that are studied over the years by researchers. Various theories are proposed to enhance PageRank in terms of its quality and computation time. This paper explains the behavior of Markov chain involved in a random surfer model from the original PageRank. A modified random surfer model is proposed, which could lead to a more predictable time for computing PageRank.

Keywords

Markov processes; Web sites; random processes; search engines; Google search; Markov chain; PageRank; Web page; modified random surfer model; search result; Asia; Computational modeling; Google; Markov processes; Probability distribution; Search engines; Web pages; Markov chain; PageRank; Random Surfer Model;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology in Asia (CITA 11), 2011 7th International Conference on

Conference_Location

Kuching, Sarawak

Print_ISBN

978-1-61284-128-1

Electronic_ISBN

978-1-61284-130-4

Type

conf

DOI

10.1109/CITA.2011.5998269

Filename

5998269